Security risk assessment using fast probabilistic power flow considering static power-frequency characteristics of power systems

Abstract Large scale blackouts in the world have aroused the study of security risk assessment (SRA) urgently. SRA difficulties brought about by uncertainties can be solved by probabilistic power flow (PPF). Conventional methods usually focus on the probabilistic density function (PDF) and the cumulative distribution function (CDF) of node voltages and branch flows only. A SRA scheme of power system using fast PPF is proposed in this paper. The scheme took static power-frequency characteristics (SPFCs) into account, and fast decoupled power flow (FDPF) was used to solve PPF. Besides node voltage and branch flows, the scheme can obtain the PDF and CDF of frequency. The computing speed of the proposed scheme considering wind power multi-scenarios is enhanced compared to conventional method. SRA indices are introduced to evaluate the power system quantitatively. The examples on the IEEE RTS-24 system demonstrate the feasibility, rapidity and validity of the proposed scheme.

[1]  Julio Usaola,et al.  Probabilistic load flow with correlated wind power injections , 2010 .

[2]  Yan Ping.  A fast load flow model for a dispatcher training simulator considering frequency deviation effects , 1998 .

[3]  A. Llombart,et al.  Statistical Analysis of Wind Power Forecast Error , 2008, IEEE Transactions on Power Systems.

[4]  O.N. Gerek,et al.  The Effect of Markov Chain State Size for Synthetic Wind Speed Generation , 2008, Proceedings of the 10th International Conference on Probablistic Methods Applied to Power Systems.

[5]  Yue Yuan,et al.  Probabilistic load flow computation of a power system containing wind farms using the method of combined cumulants and Gram-Charlier expansion , 2011 .

[6]  Probability Subcommittee,et al.  IEEE Reliability Test System , 1979, IEEE Transactions on Power Apparatus and Systems.

[7]  Yao Duan Non-member and,et al.  An improved fast decoupled power flow model considering static power–frequency characteristic of power systems with large-scale wind power , 2014 .

[8]  S. Conti,et al.  Probabilistic load flow using Monte Carlo techniques for distribution networks with photovoltaic generators , 2007 .

[9]  Buhan Zhang,et al.  An improved fast decoupled power flow model considering static power–frequency characteristic of power systems with large‐scale wind power , 2014 .

[10]  Julio Usaola.  Probabilistic load flow with wind production uncertainty using cumulants and Cornish–Fisher expansion , 2009 .

[11]  S.T. Lee,et al.  Probabilistic load flow computation using the method of combined cumulants and Gram-Charlier expansion , 2004, IEEE Transactions on Power Systems.

[12]  Z. Hu,et al.  A probabilistic load flow method considering branch outages , 2006, IEEE Transactions on Power Systems.

[13]  Roy Billinton,et al.  Reliability evaluation of power systems , 1984 .

[14]  Marko Čepin,et al.  Assessment of Power System Reliability: Methods and Applications , 2011 .

[15]  G.K. Stefopoulos,et al.  Probabilistic power flow with nonconforming electric loads , 2004, 2004 International Conference on Probabilistic Methods Applied to Power Systems.

[16]  Jing Luo,et al.  Probabilistic Load Flow Analysis Considering Power System Random Factors and Their Relevance , 2011, 2011 Asia-Pacific Power and Energy Engineering Conference.

[17]  Cheng Shijie.  Probabilistic Load Flow Algorithm Considering Static Security Risk of the Power System , 2011 .

[18]  A. Feijoo,et al.  Probabilistic Load Flow Including Wind Power Generation , 2011, IEEE Transactions on Power Systems.

[19]  A. Shamshad,et al.  First and second order Markov chain models for synthetic generation of wind speed time series , 2005 .